Journal of Engineering Technology and Applied Physics

GSM Based Door Lock Security Controlled System with Microcontroller

2026-03-08T12:53:14+08:00

This paper expresses design of a microcontroller-based GSM-based door lock security control system. The system primarily incorporates a GSM SIM 900A module, which enables communication via GSM technology, allowing the system to send and receive data, voice calls, and text messages (SMS) over a mobile network. A microprocessor is interfaced with the GSM module to regulate the door's locking and unlocking mechanism. The system was tested using a mobile phone and a DC motor, with a specific focus on the password functionality. When the correct four-digit password (1234) is entered, it is displayed as asterisks on an LCD screen for security purposes. The system is designed to be operated remotely from the owner's authorized mobile phone, providing enhanced convenience and security and eliminating the need for traditional keys, especially in cases where a key is lost or stolen. The implementation of this system offers great potential in terms of both safety and ease of use. Future improvements include replacing SMS with voice calling for even greater security. This GSM-based door lock security control system holds significant applications in various sectors, including education, safety, and economic settings. The results show 98% accuracy in door lock security while 95% success rate in opening the door to the designated areas.

A Rule-Based Approach for Oil Palm Fruit Ripeness Detection using Machine Vision

2026-03-08T13:14:30+08:00

In the oil palm industry, the grading process of oil palm fruits is conducted manually by trained inspectors via visual examination. Laborious and prone to human error, this process results in fruit ripeness being determined subjectively based on some developed standards. This study presents the development of an automatic grading system for oil palm fruit ripeness using red-green-blue (RGB) channel and rule-based classification. The grading system consists of four main stages: (i) image acquisition using camera and computer; (ii) image processing involving the segmentation of oil palm fruit; (iii) calculation of mean colour intensity based on the RGB colour model; and (iv) determination of oil palm fruit ripeness using the rule-based classification. Several features are extracted from the RGB channel and based on these extracted features; simple classification rules are formed to identify fruit ripeness. The maturity of oil palm fruits is then classified into two categories, ripe and unripe. Thirty-nine samples of oil palm fruits, comprising 21 ripe fruits and 18 unripe ones, are used to develop the classification rule. A graphical user interface (GUI) is developed using Matlab software to assist with the grading system whereby all the relevant image processing steps are coded into the GUI. The validity of the grading system is tested using nine samples of oil palm fruits (four ripe and five unripe) and the classification accuracy, sensitivity and specificity of 77.78 – 88.89%, 75%, and 80 – 100% respectively are achieved based on the established classification rule. Performance comparison with fuzzy logic indicates the promising potential of these simple classification rules as well.

Hand-Operated Ramming Tool for Casting Processes

2026-03-08T13:46:20+08:00

The production of sand molds for casting is a critical process in modern manufacturing, particularly in industries such as automotive, construction, and machinery. However, existing ramming tools and machines face several inefficiencies, including uneven compaction, high costs, and ergonomic concerns. This study focused on the design and fabrication of a manual ramming machine tailored for small-scale workshops and educational purposes, offering a low-cost and user-friendly solution for efficient mold-making. The proposed manual ramming machine is designed using SolidWorks and incorporates a 2-ton hydraulic jack to ensure uniform sand compaction. Fabrication methods included cutting, welding, and mechanical assembly using durable and recyclable materials. The final prototype is compact, portable, and manually operated, reducing energy consumption and promoting sustainability. Testing confirmed the machine’s capability to achieve uniform compression with minimal operator effort, producing high-quality molds in under one minute. The design prioritizes safety and simplicity, making it ideal for students and small-scale manufacturers. Furthermore, the incorporation of recyclable materials and manual operation underscores its environmental benefits. This research highlights the potential of the manual ramming machine to address inefficiencies in current tools, improve accessibility, and enhance the learning experience in casting workshops. Recommendations for future improvements include modular components for easier maintenance ad potential automation to reduce manual effort further.

Enhancing Spectral Quality through Multisource Fusion of SAR and Optical Imagery Using Deep Learning

2026-03-08T13:59:25+08:00

This study evaluates the effectiveness of a multi-source satellite image fusion method using deep learning to enhance spectral feature quality in urban environments. The input data consist of synthetic aperture radar (SAR) images from Sentinel 1 and optical images from Landsat 9. Two deep learning models were implemented: patch-wise shallow convolutional neural networks (CNN) and convolutional autoencoders (CAE). Each of Red-Green-Blue optical band was fused separately with the VV/VH ratio image derived from radar data. The fusion results were assessed using RMSE, SSIM, UIQI indices, and the Pearson correlation coefficient. The CAE demonstrated better spectral reconstruction capability with lower RMSE and higher SSIM across all three bands. Conversely, the CNN model achieved higher UIQI on some bands and produced images with visually superior sharpness. Visual assessment indicated that CNN better preserves edge details and fine structures, while CAE generated smoother images but with some blurring of objects. These results suggest that deep learning-based fusion methods hold great potential for improving input image quality for urban analysis in areas affected by cloud cover.

Mantis Search Algorithm Based SHEPWM for Five-Phase Multilevel Inverter

2026-03-08T14:13:32+08:00

Large electric vehicles are being introduced as replacements for conventional heavy internal combustion engine (ICE) vehicles, aiming to mitigate environmental concerns. Five-phase electric motors are suggested as propulsion units in these vehicles due to their advantages in torque smoothness and fault tolerance. Driving electric motors requires converting DC power into AC using power converters. Among the available options, multilevel inverters (MLIs) are one option that suits this power conversion process. Compared to a conventional two-level inverter, the MLI offers the advantages of producing low-distortion output AC voltage with lower voltage stress on power switches and higher overall efficiency. However, determining the switching angles for the MLI is challenging. In this work, mantis search algorithm (MSA)-based selective harmonic elimination pulse width modulation (SHEPWM) technique is proposed to compute the switching angles for a five-phase MLI. The MSA-SHEPWM method is compared to that of genetic algorithm (GA) and particle swarm optimization (PSO) in terms of the objective function. The switching angles obtained using MSA-SHEPWM are capable of producing a low-distorted AC output voltage, in which the desired fundamental voltage is sustained while undesired low-order harmonics are successfully minimized. The effectiveness of the proposed algorithm has been validated through MATLAB and PSIM simulations.

Preliminary Analysis of Swirl Effervescent Atomization Droplet Diameter Distribution

2026-03-08T15:44:58+08:00

A swirl effervescent atomizer capable of producing fine droplets with relatively low injection pressures. A critical aspect of atomization is the resultant droplet diameter distribution, which portrays the efficiency of the atomization process. Dimensional analysis was conducted to identify the most significant parameters influencing the droplet diameter distribution. Three dimensionless numbers were selected considering their importance, which are the liquid Reynolds number, Re, the gas Reynolds number, and the swirl chamber length to discharge orifice diameter ratio. A test rig was fabricated to test the atomizer. Water acts as the working fluid, and air acts as the atomization assistance. The resultant spray images were captured using the shadowgraph technique. The images were analysed for droplet diameter measurement. The liquid Reynolds number and gas Reynolds number were found to have a significant impact on the droplet diameter distribution, particularly with an increase in the percentage of fine droplets. However, the dependence of the droplet diameter distribution on the geometrical ratio is less significant. This result is important for a preliminary understanding of the swirl effervescent atomization mechanics.

Design and Fabrication of A Household-Friendly 5R Dustbin for Food Waste Composting

2026-03-08T16:01:20+08:00

Malaysia’s rapid population growth has intensified the need for effective food waste management to reduce greenhouse gas emissions such as carbon dioxide and methane. This study presents the design, fabrication, and evaluation of the 'Dustbin 5R', a compact, user-friendly composting unit aimed at household use. Incorporating the 5R principles—Reduce, Reuse, Recycle, Refuse, and Rot—the system features a low-speed, high-torque DC motor that drives a stainless-steel cutter to process food waste into compostable material. Constructed with aluminum and stainless steel for durability, the dustbin operates using a rechargeable battery system and has overall dimensions of 500 mm × 500 mm × 600 mm. The primary objective is to enable users to manage food waste at home and convert it into organic fertilizer, promoting environmental awareness and cost savings. The prototype supports a capacity of up to 5 kg of food waste and has demonstrated effective cutting performance, compost output, and operational safety during testing. Results validate its potential as a decentralized solution for urban food waste challenges. In conclusion, the Dustbin 5R is not only environmentally friendly but also practical for everyday use, offering a sustainable way for households to reduce waste and produce organic fertilizer.

Design and Fabrication of Chassis with Structural System for Student Formula Racing Car

2026-03-08T16:12:21+08:00

This study highlights the advancements in the development of a race car chassis, which serves as the structural backbone of the vehicle. The chassis plays a critical role in bearing various loads and forces, transferring them efficiently to the ground through the wheels and tires. A key challenge in chassis design is ensuring the proper placement of components to achieve optimal balance. The Formula Student Racing Car project focused on designing and fabricating a chassis that complies with competition standards and safety regulations. Balancing performance and practicality under various constraints posed significant design challenges. One of the main objectives was to construct the body frame of the Formula Student Racing Car through a series of fabrication processes, including cutting, welding, cleaning, and finishing. The final chassis, built using hollow steel bars, successfully met rigorous safety and durability requirements, sustaining no damage during testing. The project's completion within the allotted eight weeks demonstrates compliance with precise specifications, technical standards, and client expectations.

Early Detection of Fetal Distress using CTG and Machine Learning to Improve Maternal and Child Health

2026-03-08T16:40:02+08:00

This study addresses the critical challenge of fetal distress identification, with either maternal or neonatal death coming first in this case. Manual CTG interpretation, being subjective, leads to disadvantages, and a high degree of inter-observer variability may result in misdiagnosis or late clinical intervention with detrimental consequences. Thus, our research fills the gap wherein there has been very little comprehensive and rigorous comparative analysis of a large number of machine learning models. We systematically studied a wide and diverse range of classifiers in our comparison, including tree-based ensembles such as XGBoost, Random Forest, CatBoost, and LightGBM, but also Gradient Boosting, Decision Tree, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Logistic Regression, AdaBoost, Naive Bayes, and Convolutional Neural Networks (CNN). These models were trained and tested on a very large and open-source CTG dataset to validate their predictive power. Our findings reveal that the XGBoost model demonstrated superior performance with an impressive accuracy of 99.04%, while CatBoost, LightGBM, and Random Forest, which had intense predictive powers well above traditional diagnostic means. The above-mentioned accuracy-driven models have found their aptitude in capturing highly complicated nonlinear patterns occurring in CTG data and therefore hold a promising prospect to be developed and applied toward a large-scale and automated diagnostic aid. The successful implementation of these novel techniques would have huge potential for improving the quality of prenatal care and clinical decision-making in resource-poor areas, where expert supervision may be widely scarce.

Designing an Innovative Tongue Retractor Device for Oral and Maxillofacial Procedures

2026-03-08T17:01:39+08:00

Developing novel medical devices is essential for enhancing precision and accessibility for several oral and maxillofacial procedures. This study focuses on designing an advanced tongue retraction device that overcomes shortfalls in the current instruments while improving ergonomic efficiency and patient comfort. A comprehensive literature review identified key challenges, including anatomical variability, limited adaptability to diverse demographics, and unsustainable design practices. Critical factors such as gender-specific mouth-opening dimensions, tongue viscoelastic properties, and ergonomic principles were integrated into the design. The proposed device incorporates sustainable materials, adjustable design elements, and improved stabilization techniques to meet clinical demands. Using CATIA for 3D modelling and finite element analysis, the designed tongue retractor was optimized to enhance surgical access, visualization, and precision while reducing operative time and surgeon fatigue. Preliminary testing in simulated surgical environments demonstrated significant ease of use, patient comfort, and retraction stability improvements. Furthermore, the design aligns with emerging trends in sustainable product development and minimizing environmental impact. This study bridges the gap in accommodating diversity, addressing ecological concerns, and offering a transformative solution that integrates functional innovation and sustainability to improve procedural outcomes and enhance patient safety, especially for kids.

An Improved Authenticator Based Public Auditing Scheme in Cloud Storage

2026-03-08T17:13:01+08:00

To minimize costs associated with software maintenance, hardware infrastructure, and secure communication, many organizations and data owners are increasingly opting for cloud storage solutions. However, ensuring the integrity and security of data stored on cloud servers (CS) remains a significant concern. Traditional methods typically use data encryption and decryption to safeguard data integrity, requiring DOs to download, decrypt, audit, and then re-encrypt data before re-uploading it to the CS. This approach is computationally intensive and introduces vulnerabilities, such as data leakage risks, particularly if encryption keys are compromised or data is transferred over insecure channels. While several methods have been proposed to enhance data integrity and availability on cloud platforms, few comprehensively address user data privacy and security, especially in group settings where user revocation is needed. Additionally, current solutions often fail to mitigate risks to sensitive data during the audit process itself. This paper proposes a novel framework designed to protect user identity and data privacy during public auditing of cloud-stored data. By building on and improving existing methods, this framework provides enhanced data confidentiality and integrity while reducing computational and communication overhead. It is also adaptable to various cloud storage models, including Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS), thus offering a versatile and practical solution for secure cloud environments.

Optimisation of Node Placement Using Wireless Mesh for Machinery Monitoring: A Case Study in Industrial Facilities

2026-03-08T17:23:08+08:00

This study investigates the effect of sensor node placement and optimises it within a wireless mesh network for Condition-Based Monitoring (CBM) of industrial machinery. It evaluates network performance across baseline, standalone, and optimised topologies with a particular focus on total latency. Results demonstrated significant improvements in network performance, especially reducing total latency for Node 4 from 13.5 minutes to as low as 7 minutes through strategic node placement. Detailed analysis using the wireless mesh evaluation (Ev) kit showed a correlation between the Received Signal Strength Indicator (RSSI) and latency results. These findings conclude that strategic node placement can effectively minimise latency and ensure stable connectivity. This is important in order to get reliable data transmission, especially in challenging environments for wireless networks.

A Two-Equation Model for Forced Convection Heat Transfer in Porous Medium with The Effect of Heat Generation in the Solid

2026-03-09T16:03:43+08:00

This study analyses forced convection through a rectangular porous channel subject to uniform wall heat flux with internal heat generation in the solid phase, adopting a local thermal non-equilibrium model. Applying the Brinkman-extended Darcy velocity model, the forced convection problem is solved analytically, in terms of four key parameters, heat source ratio, fluid to solid effective thermal conductivity ratio, equivalent Biot number and porous medium shape factor. The exact solution is compared with one using Darcy velocity model. When the boundary effect is not taken into consideration in porous medium with low permeability, the heat transfer coefficient is overpredicted. Wall effects have also resulted in a higher critical value for the for the occurrence of dissimilarity in the sign of the fluid and solid wall temperature gradient.

Blending Optimization on The Fuel Characterization of Sunflower Oil-Pentanol Binary Biofuel Blend

2026-03-09T16:23:35+08:00

Straight vegetable oil (SVO) such as Pure Sunflower Oil (PSO) is a possible alternative biofuel to petroleum diesel fuel because of its physical qualities identical to fossil fuel. However, the higher viscosity and density of PSO limits its direct use in the combustion engines. This work presents an experimental investigation to determine and optimize the key properties of the Sunflower oil-Pentanol blend, which is potential to be used to replace the existing fossil fuel. This study proposes a binary biofuel blend of pure sunflower oil (PSO) with higher alcohol pentanol (PEN). Pentanol has lower kinematic viscosity, density, and calorific value as compared to PSO. In this study, Mixture design method is used to formulate, optimize, and predict the key properties of this binary biofuel blend. Based on mixture design analysis, a total of 8 blend ratios which varies from 100PSO and 100PEN were proposed. The developed equation and experimental data for the density, kinematic viscosity, and calorific value equations all yielded good results. The predicted values were within 0.012 to 7.14% of the experimental results, showing the accuracy of the developed equations. The blend ratio of 30PSO70PEN was determined to be the most optimal, with response values of density, kinematic viscosity, and calorific value of 875.966 kg/m³, 5.99 mm²/s, and 36068.7 kJ/kg, respectively. All these responses meet the ASTM D6751 requirements for biodiesel and are suitable for use in a Compression Ignition engine.

IoT-Based Industrial Wastewater Monitoring System using ESP32 and Blynk

2026-03-09T16:36:14+08:00

Effective industrial wastewater management is essential to mitigate the environmental and public health risks posed by harmful contaminants e.g. high TDS, turbidity, abnormal pH, and temperature. A monitoring system is crucial in each related company to ensure its wastewater will be properly treated to meet regulatory standards as that can severely impact ecosystems, aquatic life, and water resources. However, traditional industrial wastewater monitoring methods like manual sampling and laboratory analysis fail to provide real-time data and are time consuming and labour intensive. Thus, this study evaluates an Internet of Things (IoT)-based monitoring system for industrial wastewater, focusing on the measurement of four parameters, including total dissolved solids (TDS), pH, temperature, and turbidity. The system consisted of a Durian ESP32 microcontroller, a TDS sensor (SEN0244), a pH sensor (SEN0161), a turbidity sensor (SEN0189), and temperature sensor (DS18B20). Real-time monitoring, data analysis, and visualization were facilitated via the Blynk cloud platform. The accuracy and reliability of the developed system were evaluated through functionality testing, performance testing, and system testing in actual environment using textile dyeing industrial water samples. Results show that the proposed monitoring system was able to achieve measurement accuracies of 87.76% for TDS, 93.28% for pH, and 95.35% for temperature. This shows that the system is feasible in continuously monitoring the TDS, pH, and temperature of water quality in industry.

Optimal Worker Allocation of Wooden Furniture Manufacturing System using Simulation Modeling and Data Envelopment Analysis

2026-03-09T17:15:00+08:00

This study introduces the application of simulation in modeling the wooden furniture manufacturing system at the XYZ factory particularly addressing the issue of worker allocation on the production line. The XYZ factory has many workers in its furniture manufacturing system, which needs to be allocated to nine processes. The imbalance number of workers in each process will affect productivity. Simulation method is used to model the actual system. The simulation model of the actual system is verified and validated, and 45 alternatives of worker allocations are identified using Min-max operator allocation formulation. Data Envelopment Analysis - Banker, Charnes and Cooper (DEA-BCC) model is used to determine the efficiency score of each alternative. Then, DEA-cross efficiency is used to rank the alternatives. The selection criteria of the optimal worker allocation alternative are based on the total production, the average worker utilization, the average total production time, the average number of entities in the system, and the total number of workers involved in the manufacturing system. In this study, Alternative-31 (A31) is the optimal worker allocation alternative among the alternatives that have been ranked. This alternative reduces the total number of workers from 109 to 103 and decreases the average waiting time across four processes from 191.1680 to 189.7700 minutes. The application of simulation modeling, DEA-BCC and DEA-cross efficiency can help the management of the factory to make better decisions and can provide ideas to other manufacturing companies in determining the optimal worker allocation.

Evaluating The Influence of Building Typologies and Attributes on Outdoor Thermal Comfort in Urban Environments

2026-03-09T17:25:36+08:00

The variation in urban microclimate is closely related to the building's physical attributes, including building height, material composition, building condition, building size, building morphology, and building type. Changes in microclimate due to the continuous expansion of urban buildings have a significant impact on the health and comfort level of urban residents. Thus, it is necessary to understand the influence of built environment factors towards outdoor thermal comfort, particularly in tropical cities like Kuching, Sarawak. This study investigates the interplay between building typologies and microclimatic factors in Kuching, Sarawak, focusing on residential, commercial, office and educational buildings. Field measurements of air temperature, relative humidity, wind speed, and heat index were conducted over more than seven consecutive days in April 2024 using handheld environmental meters. The measurement was recorded every two hours per day at sixteen (16) static points across different types of buildings. The data were collected manually with the sensor positioned at a height of approximately 1.5 m above ground level, taking into account the average breathing zone height of a standing adult. Additional data, including human activities around each study area, were also recorded to account for the influence of these factors on the microclimate. Following the confirmation of non-normality in the dataset through a Kolmogorov-Smirnov test, a Kruskal-Wallis test was performed. The results reveal that wind speed and relative humidity vary significantly among building types (p < 0.05), whereas air temperature and heat index show a consistent statistical pattern. In the analysis, commercial areas showed the highest wind speeds, while residential areas recorded the highest relative humidity. Therefore, the result clearly underscored that the importance of building types, spatial design, materials, and methods influences outdoor thermal comfort.

YCL Based Smart Glasses for Visually Impaired

2026-03-09T17:44:08+08:00

A key component of assistive technology that helps people with visual impairments access text in their daily lives is a text reader. An image-based text recognition tool for the blind is presented in this article. It takes pictures of the user's surroundings using a dual-camera module. For improving character detection and recognition in these developed smart glasses for the blind and the visually impaired, a hybrid algorithm called the YCL Character Recognition Algorithm that combines You Only Look Once (YOLO), Convolutional Recurrent Neural Networks (CRNN), and Long Short-Term Memory (LSTM) to balance out the drawbacks of each algorithm by learning from its advantages. The suggested YOLO-v8 model is used for real-time text object detection, CRNN is used to extract character features, and LSTM is used to enhance sequential character prediction. An audio output signal is given to the user by the image processing software after the visual data has been processed. These smart glasses have the benefit of being able to view characters from both close and far distances. A specially acquired dataset was used to evaluate the suggested YCL technique, which shows notable speed and accuracy gains over the traditional homogenous algorithms. The suggested method is successful in identifying words and characters and in delivering audio output for the blind and visually impaired, based on users’ surveys and experimental data.

Optimizer Performance in Deep Learning for Leaf Disease Classification

2026-03-09T17:59:05+08:00

Global food security is at risk due to the spread of plant diseases, particularly in regions where agriculture is a significant economic activity. Early and precise identification of plant diseases helps avoid crop loss and maintain agricultural output. Here, we investigate different deep learning optimizers for the classification of leaf diseases. We employ EfficientNet-B0, a cutting-edge model built on high accuracy and efficiency in image classification tasks intended for agricultural settings with limited resources. The PlantVillage and PlantDoc databases are used to determine the best optimizer. We evaluate the results of five popular optimizers on EfficientNet-B0: Adam, Nadam, Adagrad, RMSprop, and SGD. Empirical findings indicate that Adam produces training, validation, and testing outcomes that outperform other optimizers. Future real-time agricultural application implementations are anticipated to be fueled by this insight.

Experimental Study on Wave Attenuation Performance of Recycled Materials As Floating Breakwater

2026-03-09T18:15:16+08:00

Wave modeling has become increasingly important in coastal engineering, particularly for understanding sea wave behavior and assessing the performance of protective structures. Researchers often employ flume wave makers, which allow precise replication of wave characteristics to study wave dynamics in controlled conditions. Floating breakwaters present a cost-effective alternative to conventional fixed structures, especially in areas with relatively mild wave climates, and are frequently used to protect harbors, marinas, and shorelines from erosion. In this study, the performance of three floating breakwater designs was evaluated, with each constructed from recycled plastic bottles to assess the feasibility of incorporating post-consumer materials in coastal infrastructure. The prototypes varied in submersion levels which 0% submerged, 50% submerged and 70% submerged. These models were tested under different wave conditions in amplitude and frequency, to determine their effectiveness in reducing wave height and transmission. The objective was to identify the configuration that offered the greatest wave attenuation under conditions representative of real coastal environments, thereby providing insight into the optimal design for practical application in sustainable coastal engineering.